1,721,239 research outputs found
FDR-controlling procedure for spatial data with applications to neuroimaging experiments.
No full textA note on Left-Spherically Distributed Test with covariates
Full text linkE., Kropf, S., 1998. Multivariate tests based on left-spherically distributed linear scores.
Annals of Statistics 26, 1972–1988) is extended to account for nuisance parameters,
particularly for covariates that are assumed to explain (part of) the response variables but
are not under test. An R code is available on the someMTP package in CRAN
FDR- and FWE-controlling methods using data-driven weights
No full textWeighted methods are an important feature of multiplicity control methods. The weights must usually be chosen a priori, on the basis of experimental hypotheses. Under some conditions, however, they can be chosen making use of information from the data (therefore a posteriori) while maintaining multiplicity control. In this paper we provide: (1) a review of weighted methods for familywise type I error rate (FWE) (both parametric and nonparametric) and false discovery rate (FDR) control; (2) a review of data-driven weighted methods for FWE control; (3) a new proposal for weighted FDR control (data-driven weights) under independence among variables; (4) under any type of dependence; (5) a simulation study that assesses the performance of procedure of point 4 under various conditions
Exact Multivariate Permutation Tests for Fixed Effects in Mixed-Models
Full text linkA test for the fixed effect in mixed-models is proposed. It is based on permutation strategy and is exact. The testing approach presented is very general and the class of model covered is very broad.
Multivariate responses with different type of variables (e.g. continuous, categorical and ranks) are usually tested with separated models and the overall test are usually reached trough Bonferroni-like combinations, i.e. without taking in account the joint distribution of the tests statistics. On the contrary in this approach the joint distribution is immediately obtained and the dependence among tests is taken in account in the overall test
Weighted methods controlling the multiplicity when the number of variables is much higher than the number of observations.
No full textThis work proposes innovative permutation-based procedures controlling the familywise error rate (FWE). It is proved that weighted procedures control the FWE if weights are a function of the sufficient statistic. We particularly focus on the use of additional information given by the total variance of each variable. The first proposal considers the use of weights applied to the combining functions of the closed testing procedure. The second proposal exploits this information to identify clusters upon which to apply a 'sequential gatekeeping' procedure. An application to real data is shown, and a comparative simulation study highlights its usefulness even in experimental situations with a high number of elementary hypotheses
Permutation tests for between-unit fixed effects in multivariate generalized linear mixed models
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